Semi-Automated Map Feature Addition Using Endpoint Input
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Solution Overview
Problem
Existing map creation technologies often omit new or faint topographic features due to limitations in aerial or satellite photography, requiring tedious manual editing by users to add roads or other features.
Innovation Solution
A semi-automated process that allows users to input minimal data, such as endpoints or central points, for new features, which is then used to estimate and refine their locations using both photographic analysis and existing map data, reducing human effort and increasing feature accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual editing is used to add features to a map, then feature completeness is improved, but user effort and time consumption increase significantly
Solution Approach 1:
The system performs preliminary automated analysis of aerial photographs to pre-identify potential feature locations before user input is required. This preliminary action reduces the subsequent user effort needed to confirm or correct feature placements, directly addressing the contradiction between completeness and time consumption.
Solution Approach 2:
The system enables self-service by allowing users to provide minimal input (such as endpoint coordinates) while the system automatically performs the complex task of tracing feature paths and generating complete map data. This transforms the manual editing process into a semi-automated self-service operation, significantly reducing user effort while maintaining feature completeness.
2Productivity
If automated photograph analysis is used to create maps, then productivity is improved, but measurement precision of faint or new features deteriorates
Solution Approach 1:
The system implements feedback by comparing automated analysis results with existing map data and user inputs, then iteratively refining feature location estimates. This feedback loop allows the system to maintain high productivity while improving measurement precision for faint or new features that automated analysis alone might miss or misidentify.
Solution Approach 2:
The system uses composite data sources by combining automated photograph analysis results with existing map data and user inputs to create a more robust and accurate feature identification process. This composite approach leverages the strengths of each data source to maintain both productivity and precision.
3Manufacturing precision
If users trace features by hand over photographs, then feature accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system replaces the mechanical process of manual tracing with an automated computational system that uses photograph analysis and data correlation to identify feature locations. This substitution maintains feature location accuracy while dramatically improving ease of operation, as users only need to provide minimal input rather than perform tedious manual tracing.
Data Source
AI summary
A semi-automatic map editor may allow a user to add features to a map with a minimum of effort. In one example, a user may add a road to a map by indicating where the endpoints of the road are. A system may then attempt to fill in the path of the road using the user-provided endpoints, an analysis of an aerial or satellite photograph, existing maps that show the road, or any other appropriate information. Using this information, the system may display a proposed path for the road between the endpoints that the user has indicated. The user may then fine tune and/or confirm the proposed path, thereby adding the road to the map. In addition to roads, other feature such as bodies of water, ski trails, etc., may be added in this manner.